Large Predator and Prey First Differences
Evan Doughty
8/9/2021
ung.firstdiff.max <- diff(rev(bm_quants_ung[5,]), lag = 1)
ung.firstdiff.median <- diff(rev(bm_quants_ung[3,]), lag = 1)
ung.firstdiff.min <- diff(rev(bm_quants_ung[1,]), lag = 1)
ung.firstdiff.mat <- cbind(Time = rev(intervals$ageBase[1:(nrow(intervals)-1)]),ung.firstdiff.max, ung.firstdiff.median, ung.firstdiff.min)
rownames(ung.firstdiff.mat) <- paste(rev(intervals$ageBase[1:(nrow(intervals)-1)]), "Ma", sep = " ")
ung.firstdiff.mat## Time ung.firstdiff.max ung.firstdiff.median ung.firstdiff.min
## 65 Ma 65 0.092557817 0.0452368023 0.000000000
## 63 Ma 63 0.141301489 0.2582402581 0.147167922
## 61 Ma 61 0.000000000 0.0699986182 0.140223432
## 59 Ma 59 0.000000000 0.0000000000 0.000000000
## 57 Ma 57 0.349167481 0.1762605675 -0.042768768
## 55 Ma 55 0.489711588 0.1029232635 0.000000000
## 53 Ma 53 0.000000000 0.0029583219 0.000000000
## 51 Ma 51 0.571450342 0.0314626679 0.000000000
## 49 Ma 49 0.177954666 0.1091727281 0.000000000
## 47 Ma 47 0.051682296 0.1791715371 0.064273300
## 45 Ma 45 0.056038575 0.0326878381 0.528751572
## 43 Ma 43 0.000000000 0.0232737076 0.122214919
## 41 Ma 41 0.127287814 0.2259501615 -0.320054622
## 39 Ma 39 0.064135408 0.1459134775 0.126212462
## 37 Ma 37 0.190803655 0.0644432997 0.000000000
## 35 Ma 35 0.000000000 0.0359998527 0.095393249
## 33 Ma 33 -0.400249056 0.2164271975 0.034457040
## 31 Ma 31 0.045182971 -0.1092693663 0.126724371
## 29 Ma 29 0.045182971 0.0045328455 0.000000000
## 27 Ma 27 0.206582820 -0.0037803730 0.000000000
## 25 Ma 25 -0.144355262 0.1006249720 0.000000000
## 23 Ma 23 0.013345247 0.1022657148 0.000000000
## 21 Ma 21 0.002111031 0.0446100476 0.069708633
## 19 Ma 19 0.008110824 0.0048524924 -0.008510178
## 17 Ma 17 0.000000000 0.0005592086 0.117810209
## 15 Ma 15 0.000000000 0.0238968243 0.000000000
## 13 Ma 13 0.000000000 0.0360707725 0.205272062
## 11 Ma 11 0.107175255 -0.0058689393 0.097808216
## 9 Ma 9 0.000000000 -0.0079260911 0.000000000
## 7 Ma 7 0.000000000 0.0119336570 0.000000000
## 5 Ma 5 -0.115286079 0.2031847543 0.000000000
## 3 Ma 3 -0.002111031 0.0728081257 0.207886020
pred.firstdiff.max <- diff(rev(bm_quantsPred_allcarniv[5,]), lag = 1)
pred.firstdiff.median <- diff(rev(bm_quantsPred_allcarniv[3,]), lag = 1)
pred.firstdiff.min <- diff(rev(bm_quantsPred_allcarniv[1,]), lag = 1)
pred.firstdiff.mat <- cbind(Time= rev(intervals$ageBase[1:(nrow(intervals)-1)]), pred.firstdiff.max, pred.firstdiff.median, pred.firstdiff.min)
rownames(pred.firstdiff.mat) <- paste(rev(intervals$ageBase[1:(nrow(intervals)-1)]), "Ma", sep = " ")
pred.firstdiff.mat## Time pred.firstdiff.max pred.firstdiff.median pred.firstdiff.min
## 63 Ma 63 0.11051477 -0.064389602 -0.46061401
## 61 Ma 61 0.51835829 0.194058596 0.00000000
## 59 Ma 59 0.00000000 0.086902747 0.00000000
## 57 Ma 57 0.65761266 0.352184166 0.23383005
## 55 Ma 55 0.00000000 -0.142881358 0.00000000
## 53 Ma 53 0.00000000 -0.050390953 0.00000000
## 51 Ma 51 0.06949124 0.136430584 0.00000000
## 49 Ma 49 0.00000000 -0.086941781 0.17786028
## 47 Ma 47 0.00000000 0.188459429 0.03611801
## 45 Ma 45 0.00000000 0.053800447 0.07263397
## 43 Ma 43 -0.47659314 0.000000000 0.15545135
## 41 Ma 41 0.25593371 0.264793830 0.32197521
## 39 Ma 39 0.00000000 0.041810564 0.22791018
## 37 Ma 37 0.00000000 0.000000000 -0.40614309
## 35 Ma 35 0.18062514 0.362167899 0.00000000
## 33 Ma 33 0.13600314 0.071707437 0.35605018
## 31 Ma 31 0.00000000 -0.001176597 -0.54569234
## 29 Ma 29 0.00000000 -0.001176597 0.00000000
## 27 Ma 27 0.24534895 -0.002353194 0.00000000
## 25 Ma 25 0.00000000 0.035761393 0.25566786
## 23 Ma 23 0.27895842 0.150276188 0.00000000
## 21 Ma 21 0.01784590 0.141928676 0.00000000
## 19 Ma 19 0.20211174 0.000000000 0.13653881
## 17 Ma 17 0.00000000 -0.033651455 -0.34630681
## 15 Ma 15 0.00000000 -0.018633600 0.00000000
## 13 Ma 13 0.00000000 0.109551936 0.35232484
## 11 Ma 11 0.00000000 0.087608314 -0.25464684
## 9 Ma 9 0.00000000 -0.035833794 -0.23342601
## 7 Ma 7 -0.17236488 0.012219300 0.00000000
## 5 Ma 5 -0.13594862 -0.075094149 0.00000000
## 3 Ma 3 0.00000000 -0.173542316 -0.10219561
#plot differences against each other
summary(best.fit)##
## Call:
## lm(formula = pred.firstdiff.mat[, "pred.firstdiff.max"] ~ ung.firstdiff.mat[,
## "ung.firstdiff.max"])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.52273 -0.06588 -0.04614 0.08315 0.53099
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.04614 0.03772 1.223
## ung.firstdiff.mat[, "ung.firstdiff.max"] 0.23050 0.20444 1.127
## Pr(>|t|)
## (Intercept) 0.231
## ung.firstdiff.mat[, "ung.firstdiff.max"] 0.269
##
## Residual standard error: 0.197 on 29 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.04199, Adjusted R-squared: 0.008958
## F-statistic: 1.271 on 1 and 29 DF, p-value: 0.2688
summary(best.fit)##
## Call:
## lm(formula = pred.firstdiff.mat[, "pred.firstdiff.median"] ~
## ung.firstdiff.mat[, "ung.firstdiff.median"])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.22617 -0.06247 -0.02433 0.07826 0.31942
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.03309 0.02977 1.111
## ung.firstdiff.mat[, "ung.firstdiff.median"] 0.26828 0.27335 0.981
## Pr(>|t|)
## (Intercept) 0.275
## ung.firstdiff.mat[, "ung.firstdiff.median"] 0.334
##
## Residual standard error: 0.1277 on 29 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.03215, Adjusted R-squared: -0.001225
## F-statistic: 0.9633 on 1 and 29 DF, p-value: 0.3345
summary(best.fit)##
## Call:
## lm(formula = pred.firstdiff.mat[, "pred.firstdiff.min"] ~ ung.firstdiff.mat[,
## "ung.firstdiff.min"])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.52181 -0.01716 -0.01716 0.16929 0.40165
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.01716 0.04419 0.388
## ung.firstdiff.mat[, "ung.firstdiff.min"] -0.32389 0.31761 -1.020
## Pr(>|t|)
## (Intercept) 0.701
## ung.firstdiff.mat[, "ung.firstdiff.min"] 0.316
##
## Residual standard error: 0.2258 on 29 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.03462, Adjusted R-squared: 0.001331
## F-statistic: 1.04 on 1 and 29 DF, p-value: 0.3163First Differences using NALMA bins
#get color gradient for 2Ma Bins
nalma.mark <- read.csv("/Users/emdoughty/Downloads/PPP_summary.csv")
nalma.mark <- nalma.mark[,1:3]
nalma.add <- rbind(c("Aquilian", 84, 70),c("Lancian", 70, 66),c("Puercan", 66, 64.81)); colnames(nalma.add) <- colnames(nalma.mark)
nalma.mark <- rbind(nalma.mark, nalma.add)
nalma.mark[,2] <- as.numeric(nalma.mark[,2])
nalma.mark[,3] <- as.numeric(nalma.mark[,3])
nalma.mark <- nalma.mark[order(as.numeric(nalma.mark$Max_age), decreasing = TRUE),]
rownames(nalma.mark) <- nalma.mark$NALMA_Subdivision
colnames(nalma.mark) <- c("NALMA_Subdivision", "ageBase","ageTop")
nalma.mark <- nalma.mark[,-1]
nalma.mark <- nalma.mark[,c(2,1)]
intervals <- nalma.markUngulate First Differences for Max, Median, and Min
## ung.firstdiff.max_NALMA
## Aquilian-Lancian NA
## Lancian-Puercan NA
## Puercan-Torrejonian 0.23385931
## Torrejonian-Tiffanian 0.00000000
## Tiffanian-Clarkforkian 0.00000000
## Clarkforkian-Early Wasatchian 0.34916748
## Early Wasatchian-Middle Wascatchian 0.00000000
## Middle Wascatchian-Late Wasatchian 0.00000000
## Late Wasatchian-Early Bridgerian 0.88320726
## Early Bridgerian-Middle Bridgerian 0.00000000
## Middle Bridgerian-Late Bridgerian 0.00000000
## Late Bridgerian-Early Uintan 0.28882715
## Early Uintan-Late Uintan 0.30209086
## Late Uintan-Duchesnean 0.06413541
## Duchesnean-Early Chadronian 0.13471675
## Early Chadronian-Middle Chadronian 0.02637186
## Middle Chadronian-Late Chadronian 0.05943008
## Late Chadronian-Orellan -0.42996410
## Orellan-Whitneyan 0.09036594
## Whitneyan-Early early Arikareean 0.00000000
## Early early Arikareean-Late early Arikareean 0.20658282
## Late early Arikareean-Early late Arikareean -0.12078816
## Early late Arikareean-Late late Arikareean 0.00000000
## Late late Arikareean-Early Hemingfordian 0.00000000
## Early Hemingfordian-Late Hemingfordian 0.00000000
## Late Hemingfordian-Early Barstovian 0.00000000
## Early Barstovian-Late Barstovian 0.00000000
## Late Barstovian-Early Clarendonian 0.00000000
## Early Clarendonian-Middle Clarendonian 0.10717525
## Middle Clarendonian-Late Clarendonian 0.00000000
## Late Clarendonian-Early early Hemphillian 0.00000000
## Early early Hemphillian-Late early Hemphillian 0.00000000
## Late early Hemphillian-Late Hemphillian 0.00000000
## Late Hemphillian-Latest Hemphillian 0.00000000
## Latest Hemphillian-Early Blancan -0.11739711
## Early Blancan-Late Blancan -0.08065057
## Late Blancan-Early Ivingtonian -0.11291914
## Early Ivingtonian-Middle Ivingtonian -0.18080200
## Middle Ivingtonian-Rancholabrean 0.00000000
## Rancholabrean-Holocene -0.13342161
## ung.firstdiff.median_NALMA
## Aquilian-Lancian NA
## Lancian-Puercan NA
## Puercan-Torrejonian 0.035523620
## Torrejonian-Tiffanian 0.257288801
## Tiffanian-Clarkforkian 0.123148976
## Clarkforkian-Early Wasatchian 0.188738040
## Early Wasatchian-Middle Wascatchian 0.046809721
## Middle Wascatchian-Late Wasatchian 0.000000000
## Late Wasatchian-Early Bridgerian 0.010432118
## Early Bridgerian-Middle Bridgerian 0.088845720
## Middle Bridgerian-Late Bridgerian 0.262745332
## Late Bridgerian-Early Uintan 0.143764575
## Early Uintan-Late Uintan -0.065294750
## Late Uintan-Duchesnean 0.377906609
## Duchesnean-Early Chadronian -0.158543878
## Early Chadronian-Middle Chadronian 0.170290044
## Middle Chadronian-Late Chadronian 0.058182649
## Late Chadronian-Orellan 0.014640123
## Orellan-Whitneyan 0.185838936
## Whitneyan-Early early Arikareean -0.102084131
## Early early Arikareean-Late early Arikareean 0.118929797
## Late early Arikareean-Early late Arikareean 0.055210795
## Early late Arikareean-Late late Arikareean 0.015841381
## Late late Arikareean-Early Hemingfordian 0.005283240
## Early Hemingfordian-Late Hemingfordian 0.000000000
## Late Hemingfordian-Early Barstovian 0.017581878
## Early Barstovian-Late Barstovian 0.013492076
## Late Barstovian-Early Clarendonian 0.004774752
## Early Clarendonian-Middle Clarendonian 0.036058856
## Middle Clarendonian-Late Clarendonian 0.006596664
## Late Clarendonian-Early early Hemphillian 0.001211235
## Early early Hemphillian-Late early Hemphillian 0.036618775
## Late early Hemphillian-Late Hemphillian -0.000425132
## Late Hemphillian-Latest Hemphillian 0.000425132
## Latest Hemphillian-Early Blancan 0.173937973
## Early Blancan-Late Blancan 0.171482261
## Late Blancan-Early Ivingtonian -0.060081671
## Early Ivingtonian-Middle Ivingtonian 0.030670317
## Middle Ivingtonian-Rancholabrean -0.001731479
## Rancholabrean-Holocene 0.017013785
## ung.firstdiff.min_NALMA
## Aquilian-Lancian NA
## Lancian-Puercan NA
## Puercan-Torrejonian 0.000000000
## Torrejonian-Tiffanian 0.224575223
## Tiffanian-Clarkforkian 0.062816131
## Clarkforkian-Early Wasatchian -0.042768768
## Early Wasatchian-Middle Wascatchian 0.000000000
## Middle Wascatchian-Late Wasatchian 0.000000000
## Late Wasatchian-Early Bridgerian 0.000000000
## Early Bridgerian-Middle Bridgerian 0.000000000
## Middle Bridgerian-Late Bridgerian 0.064273300
## Late Bridgerian-Early Uintan 0.000000000
## Early Uintan-Late Uintan 0.330911869
## Late Uintan-Duchesnean 0.390257752
## Duchesnean-Early Chadronian -0.264045289
## Early Chadronian-Middle Chadronian 0.096949511
## Middle Chadronian-Late Chadronian 0.000000000
## Late Chadronian-Orellan 0.026854601
## Orellan-Whitneyan 0.006046177
## Whitneyan-Early early Arikareean 0.126724371
## Early early Arikareean-Late early Arikareean 0.000000000
## Late early Arikareean-Early late Arikareean 0.000000000
## Early late Arikareean-Late late Arikareean 0.555116398
## Late late Arikareean-Early Hemingfordian -0.092112897
## Early Hemingfordian-Late Hemingfordian -0.166668858
## Late Hemingfordian-Early Barstovian -0.117325977
## Early Barstovian-Late Barstovian 0.000000000
## Late Barstovian-Early Clarendonian 0.107463845
## Early Clarendonian-Middle Clarendonian 0.000000000
## Middle Clarendonian-Late Clarendonian 0.000000000
## Late Clarendonian-Early early Hemphillian 0.000000000
## Early early Hemphillian-Late early Hemphillian 0.195616433
## Late early Hemphillian-Late Hemphillian 0.000000000
## Late Hemphillian-Latest Hemphillian 0.000000000
## Latest Hemphillian-Early Blancan 0.135004884
## Early Blancan-Late Blancan 0.184278196
## Late Blancan-Early Ivingtonian -0.111397060
## Early Ivingtonian-Middle Ivingtonian -0.286049550
## Middle Ivingtonian-Rancholabrean 0.000000000
## Rancholabrean-Holocene 0.682026670Predators First Differences for Max, Median, and Min
## pred.firstdiff.max_NALMA
## Aquilian-Lancian NA
## Lancian-Puercan NA
## Puercan-Torrejonian NA
## Torrejonian-Tiffanian 0.62887306
## Tiffanian-Clarkforkian 0.05457046
## Clarkforkian-Early Wasatchian 0.60304220
## Early Wasatchian-Middle Wascatchian 0.00000000
## Middle Wascatchian-Late Wasatchian 0.00000000
## Late Wasatchian-Early Bridgerian 0.06949124
## Early Bridgerian-Middle Bridgerian 0.00000000
## Middle Bridgerian-Late Bridgerian 0.00000000
## Late Bridgerian-Early Uintan 0.00000000
## Early Uintan-Late Uintan 0.00000000
## Late Uintan-Duchesnean -0.22065943
## Duchesnean-Early Chadronian 0.00000000
## Early Chadronian-Middle Chadronian 0.00000000
## Middle Chadronian-Late Chadronian 0.00000000
## Late Chadronian-Orellan 0.31662828
## Orellan-Whitneyan 0.00000000
## Whitneyan-Early early Arikareean 0.00000000
## Early early Arikareean-Late early Arikareean 0.52430737
## Late early Arikareean-Early late Arikareean 0.01784590
## Early late Arikareean-Late late Arikareean 0.00000000
## Late late Arikareean-Early Hemingfordian 0.00000000
## Early Hemingfordian-Late Hemingfordian 0.00000000
## Late Hemingfordian-Early Barstovian 0.07937900
## Early Barstovian-Late Barstovian 0.12273274
## Late Barstovian-Early Clarendonian 0.00000000
## Early Clarendonian-Middle Clarendonian 0.00000000
## Middle Clarendonian-Late Clarendonian 0.00000000
## Late Clarendonian-Early early Hemphillian 0.00000000
## Early early Hemphillian-Late early Hemphillian -0.21995764
## Late early Hemphillian-Late Hemphillian 0.00000000
## Late Hemphillian-Latest Hemphillian 0.00000000
## Latest Hemphillian-Early Blancan -0.12467978
## Early Blancan-Late Blancan 0.03632392
## Late Blancan-Early Ivingtonian 0.00000000
## Early Ivingtonian-Middle Ivingtonian 0.00000000
## Middle Ivingtonian-Rancholabrean 0.00000000
## Rancholabrean-Holocene -0.14480914
## pred.firstdiff.median_NALMA
## Aquilian-Lancian NA
## Lancian-Puercan NA
## Puercan-Torrejonian NA
## Torrejonian-Tiffanian 0.31844166
## Tiffanian-Clarkforkian 0.16082324
## Clarkforkian-Early Wasatchian 0.14163677
## Early Wasatchian-Middle Wascatchian -0.05039095
## Middle Wascatchian-Late Wasatchian 0.05039095
## Late Wasatchian-Early Bridgerian 0.07129736
## Early Bridgerian-Middle Bridgerian -0.07129736
## Middle Bridgerian-Late Bridgerian 0.00000000
## Late Bridgerian-Early Uintan -0.06575526
## Early Uintan-Late Uintan 0.26073158
## Late Uintan-Duchesnean 0.30259485
## Duchesnean-Early Chadronian -0.04181056
## Early Chadronian-Middle Chadronian 0.04181056
## Middle Chadronian-Late Chadronian 0.05557376
## Late Chadronian-Orellan 0.39109105
## Orellan-Whitneyan -0.15620434
## Whitneyan-Early early Arikareean 0.20552489
## Early early Arikareean-Late early Arikareean 0.06663472
## Late early Arikareean-Early late Arikareean 0.08744343
## Early late Arikareean-Late late Arikareean 0.12568647
## Late late Arikareean-Early Hemingfordian 0.02262531
## Early Hemingfordian-Late Hemingfordian -0.06566660
## Late Hemingfordian-Early Barstovian 0.03399890
## Early Barstovian-Late Barstovian -0.02459022
## Late Barstovian-Early Clarendonian 0.20840635
## Early Clarendonian-Middle Clarendonian -0.18029296
## Middle Clarendonian-Late Clarendonian 0.19471423
## Late Clarendonian-Early early Hemphillian -0.04197578
## Early early Hemphillian-Late early Hemphillian 0.01313324
## Late early Hemphillian-Late Hemphillian -0.01313324
## Late Hemphillian-Latest Hemphillian 0.12308693
## Latest Hemphillian-Early Blancan -0.27893342
## Early Blancan-Late Blancan 0.05709012
## Late Blancan-Early Ivingtonian -0.09722669
## Early Ivingtonian-Middle Ivingtonian 0.09722669
## Middle Ivingtonian-Rancholabrean 0.06737628
## Rancholabrean-Holocene -0.56298745
## pred.firstdiff.min_NALMA
## Aquilian-Lancian NA
## Lancian-Puercan NA
## Puercan-Torrejonian NA
## Torrejonian-Tiffanian 0.0000000
## Tiffanian-Clarkforkian 0.2338300
## Clarkforkian-Early Wasatchian 0.0000000
## Early Wasatchian-Middle Wascatchian 0.0000000
## Middle Wascatchian-Late Wasatchian 0.0000000
## Late Wasatchian-Early Bridgerian 0.0000000
## Early Bridgerian-Middle Bridgerian 0.1778603
## Middle Bridgerian-Late Bridgerian 0.0000000
## Late Bridgerian-Early Uintan 0.0000000
## Early Uintan-Late Uintan 0.1087520
## Late Uintan-Duchesnean 0.7053367
## Duchesnean-Early Chadronian -0.4061431
## Early Chadronian-Middle Chadronian 0.0000000
## Middle Chadronian-Late Chadronian 0.0000000
## Late Chadronian-Orellan 0.1085306
## Orellan-Whitneyan -0.2981728
## Whitneyan-Early early Arikareean 0.0000000
## Early early Arikareean-Late early Arikareean 0.2556679
## Late early Arikareean-Early late Arikareean 0.0000000
## Early late Arikareean-Late late Arikareean 0.1365388
## Late late Arikareean-Early Hemingfordian 0.0000000
## Early Hemingfordian-Late Hemingfordian 0.0000000
## Late Hemingfordian-Early Barstovian -0.3463068
## Early Barstovian-Late Barstovian 0.0000000
## Late Barstovian-Early Clarendonian 0.3523248
## Early Clarendonian-Middle Clarendonian -0.4880729
## Middle Clarendonian-Late Clarendonian 0.0000000
## Late Clarendonian-Early early Hemphillian 0.0000000
## Early early Hemphillian-Late early Hemphillian 0.0000000
## Late early Hemphillian-Late Hemphillian 0.0000000
## Late Hemphillian-Latest Hemphillian 0.0000000
## Latest Hemphillian-Early Blancan 0.0000000
## Early Blancan-Late Blancan -0.1021956
## Late Blancan-Early Ivingtonian 0.0000000
## Early Ivingtonian-Middle Ivingtonian 0.0000000
## Middle Ivingtonian-Rancholabrean 0.0000000
## Rancholabrean-Holocene 0.0000000
summary(best.fit)##
## Call:
## lm(formula = pred.firstdiff.mat_NALMA[, "pred.firstdiff.max_NALMA"] ~
## ung.firstdiff.mat_NALMA[, "ung.firstdiff.max_NALMA"])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.27169 -0.05470 -0.04206 -0.01676 0.58681
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 0.04206 0.03082
## ung.firstdiff.mat_NALMA[, "ung.firstdiff.max_NALMA"] 0.13990 0.15577
## t value Pr(>|t|)
## (Intercept) 1.365 0.181
## ung.firstdiff.mat_NALMA[, "ung.firstdiff.max_NALMA"] 0.898 0.375
##
## Residual standard error: 0.1843 on 35 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.02253, Adjusted R-squared: -0.005402
## F-statistic: 0.8066 on 1 and 35 DF, p-value: 0.3753
summary(best.fit)##
## Call:
## lm(formula = pred.firstdiff.mat_NALMA[, "pred.firstdiff.median_NALMA"] ~
## ung.firstdiff.mat_NALMA[, "ung.firstdiff.max_NALMA"])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.59769 -0.08348 0.01017 0.08751 0.36409
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 0.03817 0.02940
## ung.firstdiff.mat_NALMA[, "ung.firstdiff.max_NALMA"] 0.02599 0.14860
## t value Pr(>|t|)
## (Intercept) 1.298 0.203
## ung.firstdiff.mat_NALMA[, "ung.firstdiff.max_NALMA"] 0.175 0.862
##
## Residual standard error: 0.1758 on 35 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.0008731, Adjusted R-squared: -0.02767
## F-statistic: 0.03059 on 1 and 35 DF, p-value: 0.8622
summary(best.fit)##
## Call:
## lm(formula = pred.firstdiff.mat_NALMA[, "pred.firstdiff.min_NALMA"] ~
## ung.firstdiff.mat_NALMA[, "ung.firstdiff.max_NALMA"])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.49700 -0.01794 -0.01331 0.00097 0.69465
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 0.01331 0.03380
## ung.firstdiff.mat_NALMA[, "ung.firstdiff.max_NALMA"] -0.04092 0.17085
## t value Pr(>|t|)
## (Intercept) 0.394 0.696
## ung.firstdiff.mat_NALMA[, "ung.firstdiff.max_NALMA"] -0.240 0.812
##
## Residual standard error: 0.2022 on 35 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.001636, Adjusted R-squared: -0.02689
## F-statistic: 0.05737 on 1 and 35 DF, p-value: 0.8121
summary(best.fit)##
## Call:
## lm(formula = pred.firstdiff.mat_NALMA[, "pred.firstdiff.max_NALMA"] ~
## ung.firstdiff.mat_NALMA[, "ung.firstdiff.median_NALMA"])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.37383 -0.04783 -0.02743 -0.00499 0.51603
##
## Coefficients:
## Estimate
## (Intercept) 0.02682
## ung.firstdiff.mat_NALMA[, "ung.firstdiff.median_NALMA"] 0.33435
## Std. Error t value
## (Intercept) 0.03464 0.774
## ung.firstdiff.mat_NALMA[, "ung.firstdiff.median_NALMA"] 0.28365 1.179
## Pr(>|t|)
## (Intercept) 0.444
## ung.firstdiff.mat_NALMA[, "ung.firstdiff.median_NALMA"] 0.246
##
## Residual standard error: 0.1828 on 35 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.03818, Adjusted R-squared: 0.0107
## F-statistic: 1.39 on 1 and 35 DF, p-value: 0.2464
summary(best.fit)##
## Call:
## lm(formula = pred.firstdiff.mat_NALMA[, "pred.firstdiff.median_NALMA"] ~
## ung.firstdiff.mat_NALMA[, "ung.firstdiff.median_NALMA"])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.59526 -0.07177 0.00164 0.09341 0.35919
##
## Coefficients:
## Estimate
## (Intercept) 0.02961
## ung.firstdiff.mat_NALMA[, "ung.firstdiff.median_NALMA"] 0.15658
## Std. Error t value
## (Intercept) 0.03317 0.893
## ung.firstdiff.mat_NALMA[, "ung.firstdiff.median_NALMA"] 0.27160 0.576
## Pr(>|t|)
## (Intercept) 0.378
## ung.firstdiff.mat_NALMA[, "ung.firstdiff.median_NALMA"] 0.568
##
## Residual standard error: 0.1751 on 35 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.009406, Adjusted R-squared: -0.0189
## F-statistic: 0.3323 on 1 and 35 DF, p-value: 0.568
summary(best.fit)##
## Call:
## lm(formula = pred.firstdiff.mat_NALMA[, "pred.firstdiff.min_NALMA"] ~
## ung.firstdiff.mat_NALMA[, "ung.firstdiff.median_NALMA"])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.48325 -0.08597 0.02216 0.06986 0.47893
##
## Coefficients:
## Estimate
## (Intercept) -0.02922
## ung.firstdiff.mat_NALMA[, "ung.firstdiff.median_NALMA"] 0.67642
## Std. Error t value
## (Intercept) 0.03570 -0.818
## ung.firstdiff.mat_NALMA[, "ung.firstdiff.median_NALMA"] 0.29231 2.314
## Pr(>|t|)
## (Intercept) 0.4186
## ung.firstdiff.mat_NALMA[, "ung.firstdiff.median_NALMA"] 0.0267 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1884 on 35 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.1327, Adjusted R-squared: 0.1079
## F-statistic: 5.355 on 1 and 35 DF, p-value: 0.02666
summary(best.fit)##
## Call:
## lm(formula = pred.firstdiff.mat_NALMA[, "pred.firstdiff.max_NALMA"] ~
## ung.firstdiff.mat_NALMA[, "ung.firstdiff.min_NALMA"])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.24399 -0.05660 -0.05660 0.00325 0.60966
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 0.05660 0.03153
## ung.firstdiff.mat_NALMA[, "ung.firstdiff.min_NALMA"] -0.16647 0.15997
## t value Pr(>|t|)
## (Intercept) 1.795 0.0813 .
## ung.firstdiff.mat_NALMA[, "ung.firstdiff.min_NALMA"] -1.041 0.3052
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1836 on 35 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.03001, Adjusted R-squared: 0.002297
## F-statistic: 1.083 on 1 and 35 DF, p-value: 0.3052
summary(best.fit)##
## Call:
## lm(formula = pred.firstdiff.mat_NALMA[, "pred.firstdiff.median_NALMA"] ~
## ung.firstdiff.mat_NALMA[, "ung.firstdiff.min_NALMA"])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.56399 -0.09298 0.00781 0.09644 0.35014
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 0.04258 0.03014
## ung.firstdiff.mat_NALMA[, "ung.firstdiff.min_NALMA"] -0.06097 0.15291
## t value Pr(>|t|)
## (Intercept) 1.413 0.167
## ung.firstdiff.mat_NALMA[, "ung.firstdiff.min_NALMA"] -0.399 0.693
##
## Residual standard error: 0.1755 on 35 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.004522, Adjusted R-squared: -0.02392
## F-statistic: 0.159 on 1 and 35 DF, p-value: 0.6925